Electronic contract maintenance management method and system based on big data

Abstract

The present invention relates to the field of data processing technology, and in particular to an electronic contract maintenance management method and system based on big data, comprising the following steps: collecting electronic contract metadata in a data source, and sequentially cleaning, converting, compressing, merging, extracting features, and backing up the electronic contract metadata; performing data analysis based on the extracted features, and collating the analysis results to output electronic contract statistical data; when it is determined that the stability of maintenance management does not meet the requirements, adjusting the collection frequency of the electronic contract metadata; when it is determined that the validity of the storage space does not meet the requirements, determining the compression ratio of the electronic contract metadata; and re-determining the compression ratio of the electronic contract metadata based on the byte loss rate of the electronic contract metadata. The present invention achieves an improvement in the stability of maintenance management.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an electronic contract maintenance management method and system based on big data. Background Art

[0002] In the existing technology, an electronic contract is an agreement between two or more parties to establish, change, or terminate property-related civil rights and obligations through an electronic information network. With the development of science and technology, traditional paper contracts are gradually replaced or supplemented by electronic contracts. When signing existing electronic contracts, there is an unclear review of the contracting qualifications and performance capabilities of both parties, which leads to a high risk of signing the contract. At the same time, electronic contracts may be subject to the risk of hacker attacks, data leaks, or fraud, resulting in poor stability in the maintenance and management of electronic contracts.

[0003] Chinese Patent Publication No.: CN117114926A discloses a method and system for electronic contract maintenance management based on big data, the method is applied to an electronic contract maintenance management system based on big data, the system includes a user end and a management platform end, the method includes: obtaining a first electronic contract, the first electronic contract has an identifier of a first contract-related user, the first contract-related users include a first user and a second user in the user end, and the first user is Party A, and the second user is Party B; when both Party A and Party B have preset contracting qualifications, extracting first basic contracting information from the first electronic contract; analyzing the first demand information of Party A included in the first basic contracting information to obtain a first demand execution condition; judging whether the retrieved asset scale information of Party B meets the asset scale threshold preset based on the first demand execution condition; if so, extracting first legal contracting information from the first electronic contract; when the first legal contracting information meets the preset legal requirements, the management platform end stores and manages the first electronic contract based on a preset storage management solution. It can be seen from this that the electronic contract maintenance management method and system based on big data has the problem that due to the influence of network fluctuations, some data is missing during the data collection process, which leads to improper processing of abnormal data in the collected data, resulting in distorted analysis results, and then leading to erroneous extraction during feature extraction, thereby causing a decrease in the stability of maintenance management. Summary of the invention

[0004] To this end, the present invention provides an electronic contract maintenance management method and system based on big data, so as to overcome the problem in the prior art that due to the influence of network fluctuations, some data are missing during the data collection process, which leads to improper processing of abnormal data in the collected data, resulting in distorted analysis results, and further leading to erroneous extraction during feature extraction, thereby causing a decrease in the stability of maintenance management.

[0005] To achieve the above-mentioned purpose, the present invention provides an electronic contract maintenance management method based on big data, comprising the following steps: collecting electronic contract metadata in a data source, and sequentially cleaning, converting, compressing, merging, extracting features and backing up the electronic contract metadata; performing data analysis based on the extracted features, and organizing the analysis results to output electronic contract statistical data; judging the stability of maintenance management based on the variance of the proportion of erroneous data volume in the electronic contract statistical data; when it is judged that the stability of maintenance management does not meet the requirements, adjusting the collection frequency of the electronic contract metadata or preliminarily judging that the validity of the storage space does not meet the requirements and making a secondary judgment on the validity of the storage space based on the average output time of the electronic contract statistical data; when it is judged that the validity of the storage space does not meet the requirements, determining the compression ratio of the electronic contract metadata or adjusting the number of backup nodes based on the average extraction time of the electronic contract statistical data; compressing the electronic contract metadata in a single management cycle based on the determined compression ratio, respectively obtaining the missing byte amount of the electronic contract metadata and the total byte amount of the electronic contract metadata; and re-determining the compression ratio of the electronic contract metadata based on the byte missing rate of the electronic contract metadata.

[0006] Furthermore, the step of adjusting the collection frequency of electronic contract metadata includes:

[0007] Determine the stability of maintenance management based on the variance of the proportion of erroneous data in electronic contract statistics;

[0008] If the variance of the proportion of the amount of erroneous data in the electronic contract statistical data satisfies the first variance condition or the second variance condition, it is determined that the stability of the maintenance management does not meet the requirements;

[0009] If the variance of the proportion of erroneous data in the electronic contract statistical data only meets the first variance condition, it is preliminarily determined that the validity of the storage space does not meet the requirement, and a secondary determination is made on the validity of the storage space based on the average output time of the electronic contract statistical data;

[0010] If the variance of the proportion of the amount of erroneous data in the electronic contract statistical data only satisfies the second variance condition, the frequency of collecting the electronic contract metadata is increased;

[0011] Among them, the first variance condition is that the variance of the proportion of erroneous data in the electronic contract statistical data is greater than the preset first variance and less than or equal to the preset second variance; the second variance condition is that the variance of the proportion of erroneous data in the electronic contract statistical data is greater than the preset second variance.

[0012] Furthermore, the increase in the frequency of collecting the electronic contract metadata is determined by the difference between the variance of the proportion of erroneous data volume in the electronic contract statistical data and a preset second variance.

[0013] Furthermore, the step of determining the compression ratio of the electronic contract metadata includes:

[0014] Obtain the output duration of several electronic contract statistical data with the same data volume, and calculate the average output duration of the electronic contract statistical data;

[0015] Comparing the average output duration of the electronic contract statistical data with the preset first output duration and the preset second output duration respectively;

[0016] If the average output duration of the electronic contract statistical data is greater than the preset first output duration, the validity of the storage space is secondarily determined to be unsatisfactory;

[0017] If the average output duration of the electronic contract statistical data is greater than the preset first output duration and less than or equal to the preset second output duration, increasing the compression ratio of the electronic contract metadata;

[0018] If the average output time of the electronic contract statistical data is greater than the preset second output time, it is preliminarily determined that the access efficiency of the electronic contract statistical data does not meet the requirements, and a secondary determination is made as to whether the access efficiency of the electronic contract statistical data meets the requirements based on the average extraction time of the electronic contract statistical data.

[0019] Furthermore, the calculation formula for the average output duration of the electronic contract statistical data is:

[0020]

[0021] Among them, Z is the average output time of electronic contract statistics, X a is the interval from the start of processing to the output of the ath electronic contract statistical data with the same data volume, n is the number of electronic contract statistical data with the same data volume, and n is a natural number greater than or equal to 1.

[0022] Furthermore, the increase range of the compression ratio of the electronic contract metadata is determined by the difference between the average output duration of the electronic contract statistical data and a preset first output duration.

[0023] Furthermore, the step of adjusting the number of backup nodes includes:

[0024] Secondarily determining the access efficiency of the electronic contract statistical data based on the average extraction time of the electronic contract statistical data;

[0025] If the average extraction time of the electronic contract statistical data is greater than the preset extraction time, it is determined that the access efficiency of the electronic contract statistical data does not meet the requirements for the second time, and the number of backup nodes is increased.

[0026] Furthermore, the increase in the number of backup nodes is determined by the difference between an average extraction time of electronic contract statistical data and a preset extraction time.

[0027] Furthermore, the step of re-determining the compression ratio of the electronic contract metadata includes:

[0028] Obtaining the amount of missing bytes of the electronic contract metadata and the total amount of bytes of the electronic contract metadata respectively, and calculating the byte missing rate of the electronic contract metadata;

[0029] Comparing the byte missing rate of the electronic contract metadata with a preset missing rate;

[0030] If the byte missing rate of the electronic contract metadata is greater than the preset missing rate, it is determined that the integrity of the electronic contract metadata does not meet the requirements, and the compression ratio of the electronic contract metadata is reduced;

[0031] The reduction in the compression ratio of the electronic contract metadata is determined by the difference between the byte missing rate of the electronic contract metadata and the preset missing rate.

[0032] The present invention also provides an electronic contract maintenance management system based on big data, comprising:

[0033] A data collection module is used to collect electronic contract metadata from the data source;

[0034] a data processing module connected to the data acquisition module and used to process the electronic contract metadata to output electronic contract statistical data, including a preprocessing unit used to perform preprocessing operations on the electronic contract metadata to output preprocessed data, a feature extraction unit connected to the preprocessing unit and used to extract features from the preprocessed data to output feature data, and an analysis unit connected to the feature extraction unit and used to analyze and organize the feature data to output electronic contract statistical data;

[0035] A storage module, which is connected to the data acquisition module and the data processing module respectively, and is used to store the electronic contract metadata, the pre-processed data, the characteristic data and the electronic contract statistical data respectively;

[0036] A control module, which is respectively connected to the data acquisition module, the data processing module and the storage module, and is used to determine the electronic contract management status according to the variance of the proportion of the amount of erroneous data in the electronic contract statistical data, and determine the collection frequency of the electronic contract metadata or the validity of the storage space according to the electronic contract management status, and the validity of the storage space at each level determines the compression ratio of the electronic contract metadata or determines the number of backup nodes according to the average extraction time of the electronic contract statistical data, and after compressing the electronic contract metadata for a single management cycle according to the determined compression ratio, re-determine the compression ratio of the electronic contract metadata according to the byte loss rate of the electronic contract metadata.

[0037] Compared with the prior art, the beneficial effect of the present invention lies in that the present invention adjusts the collection frequency of electronic contract metadata according to the variance of the proportion of erroneous data volume in electronic contract statistical data. Due to the influence of network fluctuations, some data is missing during the data collection process, resulting in improper processing of abnormal data in the collected data, resulting in distortion of analysis results, and then resulting in erroneous extraction during feature extraction, which leads to insufficient stability of maintenance management. By increasing the collection frequency of electronic contract metadata, the influence of data collection errors caused by network fluctuations is reduced. By determining the compression ratio of electronic contract metadata according to the average output time of electronic contract statistical data, since a large amount of data may need to be processed simultaneously during the processing of electronic contract data, insufficient data storage space is caused, resulting in jamming of operation, and processing of data The duration is increased, and the demand for storage space of electronic contract metadata is reduced by increasing the compression ratio of electronic contract metadata. The number of backup nodes is adjusted according to the average extraction time of electronic contract statistical data. Due to possible external influences, the number of query interfaces increases, which in turn leads to an increase in data access, resulting in a decrease in the effectiveness of storage space. By increasing the number of backup nodes, the extraction rate of access data is improved, thereby reducing the occupancy of storage space. The compression ratio of electronic contract metadata is re-determined according to the byte missing rate of electronic contract metadata. Due to the excessive compression ratio of electronic contract metadata, some detailed data in the metadata is lost, resulting in insufficient integrity of the metadata. By reducing the compression ratio of electronic contract metadata, the integrity of the metadata is improved, and the stability of maintenance management is improved.

[0038] Furthermore, the method of the present invention adjusts the collection frequency of electronic contract metadata by setting a preset first variance and a preset second variance. Due to the influence of network fluctuations, some data is missing during the data collection process, resulting in improper processing of abnormal data in the collected data, resulting in distorted analysis results, and further resulting in erroneous extraction during feature extraction, thereby resulting in insufficient stability in maintenance management. By increasing the collection frequency of electronic contract metadata, the impact of data collection errors caused by network fluctuations is reduced, and the stability of maintenance management is further improved.

[0039] Furthermore, the method of the present invention determines the compression ratio of the electronic contract metadata by setting a preset first output duration and a preset second output duration. Since a large amount of data may need to be processed simultaneously during the processing of the electronic contract data, resulting in insufficient storage space for the data, which causes a jam in the operation and increases the processing time of the data, by increasing the compression ratio of the electronic contract metadata, the demand for storage space of the electronic contract metadata is reduced, thereby further achieving an improvement in the stability of maintenance and management.

[0040] Furthermore, the method of the present invention adjusts the number of backup nodes by setting a preset extraction time. Since it may be affected by external factors, the number of query interfaces increases, which in turn leads to an increase in the amount of access to data, thereby causing the effectiveness of the storage space to decrease. By increasing the number of backup nodes, the extraction rate for accessing data is improved, thereby reducing the occupancy of storage space and further achieving an improvement in the stability of maintenance management.

[0041] Furthermore, the method of the present invention redetermines the compression ratio of the electronic contract metadata by setting a preset missing rate. Since the compression ratio of the electronic contract metadata is too large, some detail data in the metadata is lost, resulting in insufficient integrity of the metadata. By reducing the compression ratio of the electronic contract metadata, the integrity of the metadata is improved, and the stability of maintenance management is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is an overall flow chart of the electronic contract maintenance management method based on big data according to an embodiment of the present invention;

[0043] Figure 2 This is an overall structural block diagram of an electronic contract maintenance management system based on big data according to an embodiment of the present invention;

[0044] Figure 3 A specific flow chart of a process of determining a compression ratio of electronic contract metadata in an electronic contract maintenance management method based on big data according to an embodiment of the present invention;

[0045] Figure 4 The present invention is a specific flow chart of the process of redetermining the compression ratio of electronic contract metadata in the electronic contract maintenance management method based on big data in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0048] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0049] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] See also Figure 1 , Figure 2 , Figure 3 as well as Figure 4 As shown, they are respectively an overall flow chart of the electronic contract maintenance management method and system based on big data, an overall structural block diagram, a specific flow chart of the process of determining the compression ratio of electronic contract metadata, and a specific flow chart of the process of re-determining the compression ratio of electronic contract metadata. The electronic contract maintenance management method based on big data of the present invention includes:

[0051] Step S1, collecting electronic contract metadata in the data source, and sequentially cleaning, converting, compressing, merging, extracting features, and backing up the electronic contract metadata;

[0052] Step S2, performing data analysis based on the extracted features, and collating the analysis results to output electronic contract statistical data;

[0053] Step S3, judging the stability of maintenance management based on the variance of the proportion of erroneous data in the electronic contract statistical data;

[0054] Step S4, when it is determined that the stability of maintenance management does not meet the requirements, the frequency of collecting electronic contract metadata is adjusted or the validity of the storage space is preliminarily determined to be not in compliance with the requirements and the validity of the storage space is re-determined based on the average output duration of the electronic contract statistical data;

[0055] Step S5, when the validity of the storage space is determined to be not in compliance with the requirement, the compression ratio of the electronic contract metadata is determined or the number of backup nodes is adjusted based on the average extraction time of the electronic contract statistical data;

[0056] Step S6, compressing the electronic contract metadata for a single management cycle based on the determined compression ratio, and obtaining the amount of missing bytes of the electronic contract metadata and the total amount of bytes of the electronic contract metadata respectively;

[0057] Step S7: re-determine the compression ratio of the electronic contract metadata based on the byte missing rate of the electronic contract metadata.

[0058] Specifically, electronic contract metadata includes the electronic contract number, the time the electronic contract was signed, and the name of the electronic contract.

[0059] Specifically, electronic contract statistics include the statistical amount of electronic contracts, the statistical time points of electronic contracts, and the statistical number of electronic contracts.

[0060] Specifically, the steps of data analysis include data feature conversion, feature dimension reduction, and feature statistics.

[0061] Specifically, the sorting step includes aggregating feature analysis results, storing the aggregated data, and outputting the stored data.

[0062] In implementation, the present invention adjusts the collection frequency of electronic contract metadata according to the variance of the proportion of erroneous data volume in electronic contract statistical data. Due to the influence of network fluctuations, some data is missing during the data collection process, resulting in improper processing of abnormal data in the collected data, resulting in distorted analysis results, and then resulting in erroneous extraction during feature extraction, which leads to insufficient stability in maintenance management. By increasing the collection frequency of electronic contract metadata, the impact of data collection errors caused by network fluctuations is reduced. By determining the compression ratio of electronic contract metadata according to the average output time of electronic contract statistical data, since a large amount of data may need to be processed simultaneously during the processing of electronic contract data, insufficient data storage space is caused, resulting in jams in operation and increased data processing time. Increasing the compression ratio of electronic contract metadata reduces the demand for storage space for electronic contract metadata. By adjusting the number of backup nodes according to the average extraction time of electronic contract statistical data, the query interface may increase due to external influences, which in turn leads to an increase in data access, resulting in a decrease in the effectiveness of storage space. By increasing the number of backup nodes, the extraction rate of access data is improved, thereby reducing the occupancy of storage space. By re-determining the compression ratio of electronic contract metadata based on the byte loss rate of electronic contract metadata, due to the excessive compression ratio of electronic contract metadata, some detailed data in the metadata is lost, resulting in insufficient integrity of the metadata. By reducing the compression ratio of electronic contract metadata, the integrity of the metadata is improved, and the stability of maintenance and management is improved.

[0063] Specifically, the steps for adjusting the frequency of collecting electronic contract metadata include:

[0064] Determine the stability of maintenance management based on the variance of the proportion of erroneous data in electronic contract statistics;

[0065] If the variance of the proportion of the amount of erroneous data in the electronic contract statistical data satisfies the first variance condition or the second variance condition, it is determined that the stability of the maintenance management does not meet the requirements;

[0066] If the variance of the proportion of erroneous data in the electronic contract statistical data only meets the first variance condition, it is preliminarily determined that the validity of the storage space does not meet the requirement, and a secondary determination is made on the validity of the storage space based on the average output time of the electronic contract statistical data;

[0067] If the variance of the proportion of the amount of erroneous data in the electronic contract statistical data only satisfies the second variance condition, the frequency of collecting the electronic contract metadata is increased;

[0068] Among them, the first variance condition is that the variance of the proportion of erroneous data in the electronic contract statistical data is greater than the preset first variance and less than or equal to the preset second variance; the second variance condition is that the variance of the proportion of erroneous data in the electronic contract statistical data is greater than the preset second variance.

[0069] It can be understood that the three intervals corresponding to the preset first variance and the preset second variance correspond to three situations respectively. The first situation is that the variance of the proportion of erroneous data in the electronic contract statistical data is less than or equal to the preset first variance, and it is judged that the stability of maintenance management meets the requirements; the second situation is that the variance of the proportion of erroneous data in the electronic contract statistical data is greater than the preset first variance and less than or equal to the preset second variance, and it is preliminarily judged that the validity of the storage space does not meet the requirements, and a secondary judgment is made on the validity of the storage space based on the average output time of the electronic contract statistical data; the third situation is that the variance of the proportion of erroneous data in the electronic contract statistical data is greater than the preset second variance, and the collection frequency of electronic contract metadata is adjusted.

[0070] Preferably, the first variance Q1 is preset to be 0.15, and the second variance Q2 is preset to be 0.2.

[0071] Specifically, the variance of the proportion of erroneous data in electronic contract statistics is denoted as Q.

[0072] Specifically, the variance of the proportion of erroneous data volume in electronic contract statistical data is the variance of the proportion of erroneous data volume in electronic contract statistical data within several management cycles. The calculation method of the variance of the proportion of erroneous data volume in electronic contract statistical data is a conventional technical means well known to those skilled in the art. Therefore, the calculation process of the variance of the proportion of erroneous data volume in electronic contract statistical data will not be repeated here.

[0073] Specifically, the calculation formula for the percentage of erroneous data in electronic contract statistics is:

[0074]

[0075] Among them, S is the proportion of erroneous data in electronic contract statistical data, T1 is the amount of erroneous data in electronic contract statistical data, and T2 is the total amount of data in electronic contract statistical data.

[0076] Specifically, the error in the electronic contract statistical data means that the electronic contract statistical data is different from the standard output electronic contract statistical data in terms of the byte amount of the electronic contract statistical data, the data amount of the electronic contract statistical data, and the statistical number of electronic contracts.

[0077] In implementation, the method of the present invention determines the stability of maintenance management by setting a preset first variance and a preset second variance, thereby reducing the impact of decreased accuracy of maintenance management due to inaccurate judgment of the stability of maintenance management, and further improving the stability of maintenance management.

[0078] Specifically, the increase in the frequency of collecting the electronic contract metadata is determined by the difference between the variance of the proportion of erroneous data volume in the electronic contract statistical data and a preset second variance.

[0079] Specifically, the calculation formula for the increased collection frequency of electronic contract metadata is:

[0080]

[0081] Among them, V' is the increased collection frequency of electronic contract metadata, V is the current collection frequency of electronic contract metadata, A is the minimum unit variance for adjusting the collection frequency of electronic contract metadata, and v is the collection frequency of electronic contract metadata when the variance of the proportion of erroneous data in electronic contract statistical data is the minimum unit variance.

[0082] In practice, the value of A is generally 0.2-0.3, and preferably, the preferred embodiment of A is 0.25.

[0083] In implementation, the optional range of the value of v is [3min, 5min]. Preferably, considering that when the collection frequency of electronic contract metadata is less than 4min, the collected data may be redundant, the preferred embodiment of v is 4min.

[0084] In implementation, the method of the present invention adjusts the collection frequency of electronic contract metadata by setting a preset first variance and a preset second variance. Due to the influence of network fluctuations, some data is missing during the data collection process, resulting in improper processing of abnormal data in the collected data, resulting in distorted analysis results, and further resulting in erroneous extraction during feature extraction, thereby resulting in insufficient stability in maintenance management. By increasing the collection frequency of electronic contract metadata, the impact of data collection errors caused by network fluctuations is reduced, and the stability of maintenance management is further improved.

[0085] Specifically, the step of determining the compression ratio of the electronic contract metadata includes:

[0086] Obtain the output duration of several electronic contract statistical data with the same data volume, and calculate the average output duration of the electronic contract statistical data;

[0087] Comparing the average output duration of the electronic contract statistical data with the preset first output duration and the preset second output duration respectively;

[0088] If the average output duration of the electronic contract statistical data is greater than the preset first output duration, the validity of the storage space is secondarily determined to be unsatisfactory;

[0089] If the average output duration of the electronic contract statistical data is greater than the preset first output duration and less than or equal to the preset second output duration, increasing the compression ratio of the electronic contract metadata;

[0090] If the average output time of the electronic contract statistical data is greater than the preset second output time, it is preliminarily determined that the access efficiency of the electronic contract statistical data does not meet the requirements, and a secondary determination is made as to whether the access efficiency of the electronic contract statistical data meets the requirements based on the average extraction time of the electronic contract statistical data.

[0091] It can be understood that the three intervals corresponding to the preset first output time and the preset second output time correspond to three situations respectively. The first situation is that the average output time of the electronic contract statistical data is less than or equal to the preset first output time, and the secondary judgment that the validity of the storage space meets the requirements; the second situation is that the average output time of the electronic contract statistical data is greater than the preset first output time and less than or equal to the preset second output time, and the compression ratio of the electronic contract metadata is determined; the third situation is that the average output time of the electronic contract statistical data is greater than the preset second output time, and it is preliminarily judged that the access efficiency of the electronic contract statistical data does not meet the requirements, and a secondary judgment is made on whether the access efficiency of the electronic contract statistical data meets the requirements based on the average extraction time of the electronic contract statistical data.

[0092] Preferably, the first output duration P1 is preset to be 2 seconds, and the second output duration P2 is preset to be 3 seconds.

[0093] Specifically, the average output duration of the electronic contract statistical data is recorded as P, and the difference between the average output duration of the electronic contract statistical data and the preset first output duration is recorded as △P, and △P is set to P-P1.

[0094] In implementation, the method of the present invention performs a secondary determination on the validity of the storage space by setting a preset first output duration and a preset second output duration, thereby reducing the impact of the decreased stability of maintenance management due to inaccurate secondary determination of the validity of the storage space, and further improving the stability of maintenance management.

[0095] Specifically, the calculation formula for the average output duration of the electronic contract statistical data is:

[0096]

[0097] Among them, Z is the average output time of electronic contract statistics, X a is the interval from the start of processing to the output of the ath electronic contract statistical data with the same data volume, n is the number of electronic contract statistical data with the same data volume, and n is a natural number greater than or equal to 1.

[0098] Specifically, the increase range of the compression ratio of the electronic contract metadata is determined by the difference between the average output duration of the electronic contract statistical data and the preset first output duration.

[0099] Specifically, the calculation formula for the compression ratio of the increased electronic contract metadata is:

[0100]

[0101] Among them, H' is the compression ratio of the increased electronic contract metadata, H is the current compression ratio of the electronic contract metadata, B is the minimum unit output time for adjusting the compression ratio of the electronic contract metadata, and h is the compression ratio of the electronic contract metadata when the average output time of the electronic contract statistical data is the minimum unit output time.

[0102] In practice, the value of B is generally 4 seconds to 6 seconds. Preferably, the preferred embodiment of B is 4 seconds.

[0103] In practice, the optional value range of h is [50%, 70%]. Preferably, considering that when the compression ratio of the electronic contract metadata is greater than 60%, it may cause the electronic contract to occupy too much space, the preferred embodiment of h is 60%.

[0104] In implementation, the method of the present invention determines the compression ratio of the electronic contract metadata by setting a preset first output duration and a preset second output duration. Since a large amount of data may need to be processed simultaneously during the processing of the electronic contract data, resulting in insufficient storage space for the data, which causes a jam in the operation and increases the processing time of the data. By increasing the compression ratio of the electronic contract metadata, the demand for storage space for the electronic contract metadata is reduced, thereby further achieving improved stability in maintenance and management.

[0105] Specifically, the step of adjusting the number of backup nodes includes:

[0106] Secondarily determining the access efficiency of the electronic contract statistical data based on the average extraction time of the electronic contract statistical data;

[0107] If the average extraction time of the electronic contract statistical data is greater than the preset extraction time, it is determined that the access efficiency of the electronic contract statistical data does not meet the requirements for the second time, and the number of backup nodes is increased.

[0108] It can be understood that the two intervals corresponding to the preset extraction time correspond to two situations respectively. The first situation is that the average extraction time of electronic contract statistical data is less than or equal to the preset extraction time, and the secondary judgment is that the access efficiency of the electronic contract statistical data meets the requirements; the second situation is that the average extraction time of electronic contract statistical data is greater than the preset extraction time, and the number of backup nodes is adjusted.

[0109] Preferably, the preset extraction time length Y0=0.3s.

[0110] Specifically, the average extraction time of electronic contract statistical data is recorded as Y, and the difference between the average extraction time of electronic contract statistical data and the preset extraction time is recorded as △Y, and △Y=Y-Y0 is set.

[0111] Specifically, the calculation formula for the average extraction time of electronic contract statistics is:

[0112]

[0113] Among them, Y is the average extraction time of electronic contract statistics, E f is the extraction duration of accessing electronic contract statistical data in the fth access cycle, c is the number of access cycles, and c is a natural number greater than or equal to 1.

[0114] In implementation, the method of the present invention performs a secondary judgment on the access efficiency of electronic contract statistical data by setting a preset extraction time, thereby reducing the impact of the decreased stability of maintenance management due to inaccurate secondary judgment on the access efficiency of electronic contract statistical data, and further improving the stability of maintenance management.

[0115] Specifically, the increase in the number of backup nodes is determined by the difference between the average extraction time of the electronic contract statistical data and the preset extraction time.

[0116] Specifically, the calculation formula for the increased number of backup nodes is:

[0117]

[0118] Among them, L' is the number of backup nodes after increase, L is the current number of backup nodes, C is the minimum unit extraction time for adjusting the number of backup nodes, and l is the number of backup nodes when the average extraction time of electronic contract statistical data is the minimum unit extraction time.

[0119] In practice, the value of C is generally 1.5s-2.5s, and preferably, the preferred embodiment of C is 2s.

[0120] In implementation, the optional range of the value of l is [15, 25]. Preferably, considering that when the number of backup nodes is less than 20, data loss may occur, the preferred embodiment of l is 20.

[0121] In implementation, the method of the present invention adjusts the number of backup nodes by setting a preset extraction time. Since it may be affected by external factors, the number of query interfaces increases, which in turn leads to an increase in the amount of access to data, thereby causing the effectiveness of the storage space to decrease. By increasing the number of backup nodes, the extraction rate for accessing data is increased, thereby reducing the occupancy of storage space and further achieving improved stability in maintenance management.

[0122] Specifically, the step of re-determining the compression ratio of the electronic contract metadata includes:

[0123] Obtaining the amount of missing bytes of the electronic contract metadata and the total amount of bytes of the electronic contract metadata respectively, and calculating the byte missing rate of the electronic contract metadata;

[0124] Comparing the byte missing rate of the electronic contract metadata with a preset missing rate;

[0125] If the byte missing rate of the electronic contract metadata is greater than the preset missing rate, it is determined that the integrity of the electronic contract metadata does not meet the requirements, and the compression ratio of the electronic contract metadata is reduced.

[0126] It can be understood that the two intervals corresponding to the preset missing rate correspond to two situations respectively. The first situation is that the byte missing rate of the electronic contract metadata is less than or equal to the preset missing rate, and it is judged that the integrity of the electronic contract metadata meets the requirements; the second situation is that the byte missing rate of the electronic contract metadata is greater than the preset missing rate, and the compression ratio of the electronic contract metadata is re-determined.

[0127] Preferably, the preset missing rate R0=0.1.

[0128] Specifically, the byte missing rate of the electronic contract metadata is denoted as R, the difference between the byte missing rate of the electronic contract metadata and the preset missing rate is denoted as △R, and △R=R-R0 is set.

[0129] Specifically, the calculation formula for the byte missing rate of electronic contract metadata is:

[0130]

[0131] Wherein, R is the byte missing rate of electronic contract metadata, T1 is the number of missing bytes of electronic contract metadata, and T2 is the total number of bytes of electronic contract metadata.

[0132] In implementation, the method of the present invention determines the integrity of the electronic contract metadata by setting a preset missing rate, thereby reducing the impact of the decline in the stability of maintenance management due to inaccurate determination of the integrity of the electronic contract metadata, and further improving the stability of maintenance management.

[0133] Specifically, the reduction degree of the compression ratio of the electronic contract metadata is determined by the difference between the byte missing rate of the electronic contract metadata and the preset missing rate.

[0134] Specifically, the calculation formula for the compression ratio of the reduced electronic contract metadata is:

[0135]

[0136] Wherein, H” is the compression ratio of the electronic contract metadata after reduction, D is the minimum unit loss rate for redetermining the compression ratio of the electronic contract metadata, and h' is the compression ratio of the electronic contract metadata when the byte loss rate of the electronic contract metadata is the minimum unit loss rate.

[0137] In practice, the value of D is generally 0.15-0.25, and preferably, the preferred embodiment of D is 0.2.

[0138] In practice, the optional range of h' value is [75%, 85%]. Preferably, considering that when the compression ratio of electronic contract metadata is greater than 80%, some details in the metadata may be lost, the preferred embodiment of h' is 80%.

[0139] In implementation, the method of the present invention redetermines the compression ratio of the electronic contract metadata by setting a preset missing rate. Since the compression ratio of the electronic contract metadata is too large, some detail data in the metadata is lost, resulting in insufficient integrity of the metadata. By reducing the compression ratio of the electronic contract metadata, the integrity of the metadata is improved, and the stability of maintenance management is further improved.

[0140] An electronic contract maintenance management system based on big data, comprising:

[0141] A data collection module is used to collect electronic contract metadata from the data source;

[0142] a data processing module connected to the data acquisition module and used to process the electronic contract metadata to output electronic contract statistical data, including a preprocessing unit used to perform preprocessing operations on the electronic contract metadata to output preprocessed data, a feature extraction unit connected to the preprocessing unit and used to extract features from the preprocessed data to output feature data, and an analysis unit connected to the feature extraction unit and used to analyze and organize the feature data to output electronic contract statistical data;

[0143] A storage module, which is connected to the data acquisition module and the data processing module respectively, and is used to store the electronic contract metadata, the pre-processed data, the characteristic data and the electronic contract statistical data respectively;

[0144] A control module, which is respectively connected to the data acquisition module, the data processing module and the storage module, and is used to determine the electronic contract management status according to the variance of the proportion of the amount of erroneous data in the electronic contract statistical data, and determine the collection frequency of the electronic contract metadata or the validity of the storage space according to the electronic contract management status, and the validity of the storage space at each level determines the compression ratio of the electronic contract metadata or determines the number of backup nodes according to the average extraction time of the electronic contract statistical data, and after compressing the electronic contract metadata for a single management cycle according to the determined compression ratio, re-determine the compression ratio of the electronic contract metadata according to the byte loss rate of the electronic contract metadata.

[0145] Specifically, the pre-processed data includes summary data of electronic contract amounts, calculation data of electronic contract terms, and change data of electronic contracts.

[0146] Specifically, the characteristic data includes the byte volume of the electronic contract, the keywords of the electronic contract, and the date of the electronic contract.

[0147] Example 1

[0148] This embodiment 1 uses an electronic contract maintenance management method based on big data to manage electronic contracts, and adjusts the collection frequency of electronic contract metadata according to the difference between the variance of the proportion of erroneous data volume in the electronic contract statistical data and a preset second variance, wherein the minimum unit variance for adjusting the collection frequency of electronic contract metadata is denoted as A, and the collection frequency of electronic contract metadata when the variance of the proportion of erroneous data volume in the electronic contract statistical data is the minimum unit variance is denoted as v, wherein A=0.25, v=4min, V=2min, Q=0.3, Q2=0.2.

[0149] Calculated

[0150] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An electronic contract maintenance management method based on big data, characterized in that: The following steps are involved: Collecting electronic contract metadata from the data source, and sequentially cleaning, converting, compressing, merging, extracting features, and backing up the electronic contract metadata; Perform data analysis based on the extracted features and organize the analysis results to output electronic contract statistics; The stability of maintenance management is determined based on the variance of the proportion of erroneous data in the electronic contract statistical data; When it is determined that the stability of maintenance management does not meet the requirements, the frequency of collecting electronic contract metadata is adjusted or it is preliminarily determined that the validity of the storage space does not meet the requirements and a secondary determination is made on the validity of the storage space based on the average output time of the electronic contract statistical data; When the validity of the storage space is determined to be unsatisfactory, the compression ratio of the electronic contract metadata is determined or the number of backup nodes is adjusted based on the average extraction time of the electronic contract statistical data; After compressing the electronic contract metadata for a single management cycle based on the determined compression ratio, respectively obtain the amount of missing bytes of the electronic contract metadata and the total amount of bytes of the electronic contract metadata; Re-determine the compression ratio of the electronic contract metadata based on the byte missing rate of the electronic contract metadata; The steps for adjusting the frequency of collecting electronic contract metadata include: Determine the stability of maintenance management based on the variance of the proportion of erroneous data in electronic contract statistics; If the variance of the proportion of the amount of erroneous data in the electronic contract statistical data satisfies the first variance condition or the second variance condition, it is determined that the stability of the maintenance management does not meet the requirements; If the variance of the proportion of erroneous data in the electronic contract statistical data only meets the first variance condition, it is preliminarily determined that the validity of the storage space does not meet the requirement, and a secondary determination is made on the validity of the storage space based on the average output time of the electronic contract statistical data; If the variance of the proportion of the amount of erroneous data in the electronic contract statistical data only satisfies the second variance condition, the frequency of collecting the electronic contract metadata is increased; The first variance condition is that the variance of the proportion of erroneous data in the electronic contract statistical data is greater than the preset first variance and less than or equal to the preset second variance; the second variance condition is that the variance of the proportion of erroneous data in the electronic contract statistical data is greater than the preset second variance; The step of determining the compression ratio of the electronic contract metadata includes: Obtain the output duration of several electronic contract statistical data with the same data volume, and calculate the average output duration of the electronic contract statistical data; Comparing the average output duration of the electronic contract statistical data with the preset first output duration and the preset second output duration respectively; If the average output duration of the electronic contract statistical data is greater than the preset first output duration, the validity of the storage space is secondarily determined to be unsatisfactory; If the average output duration of the electronic contract statistical data is greater than the preset first output duration and less than or equal to the preset second output duration, increasing the compression ratio of the electronic contract metadata; If the average output time of the electronic contract statistical data is greater than the preset second output time, it is preliminarily determined that the access efficiency of the electronic contract statistical data does not meet the requirements, and a secondary determination is made as to whether the access efficiency of the electronic contract statistical data meets the requirements based on the average extraction time of the electronic contract statistical data.

2. The electronic contract maintenance management method based on big data according to claim 1 is characterized in that: The increase in the frequency of collecting the electronic contract metadata is determined by the difference between the variance of the proportion of the amount of erroneous data in the electronic contract statistical data and a preset second variance.

3. The electronic contract maintenance management method based on big data according to claim 2 is characterized in that: The calculation formula for the average output duration of the electronic contract statistical data is: Among them, Z is the average output time of electronic contract statistics, X a is the interval from the start of processing to the output of the ath electronic contract statistical data with the same data volume, n is the number of electronic contract statistical data with the same data volume, and n is a natural number greater than or equal to 1.

4. The electronic contract maintenance management method based on big data according to claim 3 is characterized in that: The increase range of the compression ratio of the electronic contract metadata is determined by the difference between the average output duration of the electronic contract statistical data and the preset first output duration.

5. The electronic contract maintenance management method based on big data according to claim 4 is characterized in that: The step of adjusting the number of backup nodes includes: Secondarily determining the access efficiency of the electronic contract statistical data based on the average extraction time of the electronic contract statistical data; If the average extraction time of the electronic contract statistical data is greater than the preset extraction time, it is determined that the access efficiency of the electronic contract statistical data does not meet the requirements for the second time, and the number of backup nodes is increased.

6. The electronic contract maintenance management method based on big data according to claim 5 is characterized in that: The increase in the number of backup nodes is determined by the difference between the average extraction time of the electronic contract statistical data and the preset extraction time.

7. The electronic contract maintenance management method based on big data according to claim 6 is characterized in that: The step of re-determining the compression ratio of the electronic contract metadata includes: Obtaining the amount of missing bytes of the electronic contract metadata and the total amount of bytes of the electronic contract metadata respectively, and calculating the byte missing rate of the electronic contract metadata; Comparing the byte missing rate of the electronic contract metadata with a preset missing rate; If the byte missing rate of the electronic contract metadata is greater than the preset missing rate, it is determined that the integrity of the electronic contract metadata does not meet the requirements, and the compression ratio of the electronic contract metadata is reduced; The reduction in the compression ratio of the electronic contract metadata is determined by the difference between the byte missing rate of the electronic contract metadata and the preset missing rate.

8. An electronic contract maintenance management system using the electronic contract maintenance management method based on big data according to any one of claims 1 to 7, characterized in that: include: A data collection module is used to collect electronic contract metadata from the data source; a data processing module connected to the data acquisition module and used to process the electronic contract metadata to output electronic contract statistical data, including a preprocessing unit used to perform preprocessing operations on the electronic contract metadata to output preprocessed data, a feature extraction unit connected to the preprocessing unit and used to extract features from the preprocessed data to output feature data, and an analysis unit connected to the feature extraction unit and used to analyze and organize the feature data to output electronic contract statistical data; A storage module, which is connected to the data acquisition module and the data processing module respectively, and is used to store the electronic contract metadata, the pre-processed data, the characteristic data and the electronic contract statistical data respectively; A control module, which is respectively connected to the data acquisition module, the data processing module and the storage module, and is used to determine the electronic contract management status according to the variance of the proportion of the amount of erroneous data in the electronic contract statistical data, and determine the collection frequency of the electronic contract metadata or the validity of the storage space according to the electronic contract management status, and the validity of the storage space at each level determines the compression ratio of the electronic contract metadata or determines the number of backup nodes according to the average extraction time of the electronic contract statistical data, and after compressing the electronic contract metadata for a single management cycle according to the determined compression ratio, re-determine the compression ratio of the electronic contract metadata according to the byte loss rate of the electronic contract metadata.

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